One of the quantum computing models which can be used for finding solutions to combinatorial optimization problems is the Adiabatic Quantum Optimization model (AQO). AQO refers to the use of the adiabatic theorem in quantum mechanics [1]}, [2]} to adiabatically move towards the ground state of an interaction Hamiltonian onto which an optimization problem is mapped. The architecture which approximately implements the adiabatic quantum computer paradigm is the quantum annealer [3]}. Differently from a quantum gate architecture, on which a computation is defined by applying separate gates (operators) to each single qubit, a quantum annealer defines a Hamiltonian operator which acts simultaneously on all qubits in the ground state and adiabatically changes this operator towards a Hamiltonian on which the cost function of a specific optimization problem is mapped [4]}. As a result of the quantum mechanics adiabatic theorem, the theoretical resulting state after the adiabatic evolution will be the ground state of the final Hamiltonian, i.e. the global minimum of the optimization problem mapped onto the final Hamiltonian.
